Dieses Dokument demonstriert einen umfassenden Workflow für Zeitreihenprognosen durch die Anreicherung von Verkaufsdaten mit Feiertagsereignissen. Der Prozess umfasst:
Erforderlichen Pakete laden.
library(tidyverse)
library(tidymodels)
library(lubridate)
library(timetk)
library(modeltime)
library(prophet)Zunächst laden wir die Feiertagsdaten und führen sie mit den
Verkaufsdaten zusammen. Das holiday_flag wird erstellt, um
anzuzeigen, ob ein Datum einem Feiertagsereignis entspricht.
### Load the holidays events data
holidays_events <- read_csv("data/holidays_events.csv")## Rows: 350 Columns: 6
## ── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): type, locale, locale_name, description
## lgl (1): transferred
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.party_df <- read_csv("data/Party.csv")## Rows: 1684 Columns: 6
## ── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): family
## dbl (4): id, store_nbr, sales, onpromotion
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.party_holidays_promo <- party_df %>%
left_join(holidays_events, by = "date") %>%
mutate(holiday_flag = ifelse(is.na(type), 0, 1))Anschließend teilen wir die Daten mithilfe einer Zeitreihenaufteilung in Trainings- und Testdatensätze auf. Der Testdatensatz ist so eingestellt, dass er die Daten eines Jahres auswertet und gleichzeitig einen kumulativen Trainingsdatensatz beibehält.
splits <- time_series_split(
party_holidays_promo,
assess = "1 year",
cumulative = TRUE
)## Using date_var: date## Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.Wir werden drei verschiedene Modelle trainieren: Prophet, Prophet Boost und ein lineares Regressionsmodell (LM).
prophet_model <- prophet_reg() %>%
set_engine("prophet") %>%
fit(
sales ~ date + onpromotion + holiday_flag,
data = training(splits)
)## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.prophet_boost_model <- prophet_boost() %>%
set_engine("prophet_xgboost") %>%
fit(
sales ~ date + as.numeric(date) + month(date, label = TRUE) + onpromotion + holiday_flag,
data = training(splits)
)## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.lm_model <- linear_reg() %>%
set_engine("lm") %>%
fit(
sales ~ as.numeric(date) + month(date, label = TRUE) + onpromotion + holiday_flag,
data = training(splits)
)Die Modelle werden dann in einer Modell-Zeit-Tabelle kombiniert und anhand der Testdaten kalibriert.
model_tbl <- modeltime_table(
prophet_model,
prophet_boost_model,
lm_model
)
calibration_tbl <- model_tbl %>%
modeltime_calibrate(testing(splits))Modellleistung anhand der Genauigkeitstabelle überprüfen:
calibration_tbl %>%
modeltime_accuracy() %>%
table_modeltime_accuracy(resizable = TRUE, bordered = TRUE)## ℹ We have detected a possible intermittent series, you can change the default metric set to the extended_forecast_accuracy_metric_set() containing the MAAPE metric, which is more appropriate for this type of series.Prognostizieren Sie die zukünftigen Werte mit den kalibrierten Modellen und visualisieren Sie die Ergebnisse.
calibration_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = party_holidays_promo,
conf_interval = 0.95
) %>%
plot_modeltime_forecast(.legend_show = TRUE, .legend_max_width = 25)Kalibrierten Modelle mithilfe des gesamten Datensatzes und die Prognosen auf Grundlage der Testdaten:
refit_tbl <- calibration_tbl %>%
modeltime_refit(data = party_holidays_promo)## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
## Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.forecast_tbl <- refit_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = party_holidays_promo,
conf_interval = 0.95
)
### Plot interactive forecast if supported by your output device
forecast_tbl %>%
plot_modeltime_forecast(.interactive = TRUE)Hier führen wir eine Modellmittelung durch, indem wir die Prognosen (ohne die tatsächlichen Werte) gruppieren und die mittlere Vorhersage zusammen mit Konfidenzintervallen berechnen.
mean_forcast_tbl <- forecast_tbl %>%
filter(.key != "actual") %>%
group_by(.key, .index) %>% # Group all predictions for one date
summarise(across(.value:.conf_hi, mean)) %>% # Compute the mean for each forecast metric
mutate(
.model_id = 3, # Denotes the number of models used
.model_desc = "Average of Models"
)## `summarise()` has grouped output by '.key'. You can override using the
## `.groups` argument.### Combine actual values with the averaged forecasts and plot
forecast_tbl %>%
filter(.key == "actual") %>%
bind_rows(mean_forcast_tbl) %>%
plot_modeltime_forecast()Überprüfen Sie die numerischen Prognosewerte
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## [1579] 13.000000 19.000000 20.000000 7.000000 8.000000 8.000000
## [1585] 6.000000 17.000000 11.000000 11.000000 12.000000 8.000000
## [1591] 7.000000 1.000000 4.000000 11.000000 6.000000 6.000000
## [1597] 8.000000 15.000000 13.000000 9.000000 5.000000 3.000000
## [1603] 14.000000 7.000000 6.000000 7.000000 5.000000 6.000000
## [1609] 8.000000 23.000000 18.000000 27.000000 7.000000 6.000000
## [1615] 11.000000 8.000000 15.000000 22.000000 7.000000 7.000000
## [1621] 7.000000 12.000000 13.000000 27.000000 24.000000 12.000000
## [1627] 8.000000 38.000000 11.000000 23.000000 22.000000 14.000000
## [1633] 7.000000 5.000000 7.000000 10.000000 16.000000 28.000000
## [1639] 13.000000 5.000000 3.000000 7.000000 108.000000 0.000000
## [1645] 23.000000 16.000000 10.000000 17.000000 10.000000 7.000000
## [1651] 11.000000 29.000000 13.000000 16.000000 11.000000 9.000000
## [1657] 7.000000 19.000000 49.000000 13.000000 13.000000 13.000000
## [1663] 6.000000 10.000000 28.000000 18.000000 32.000000 21.000000
## [1669] 12.000000 10.000000 10.000000 11.000000 14.000000 18.000000
## [1675] 35.000000 43.000000 20.000000 14.000000 18.000000 14.000000
## [1681] 10.000000 8.000000 34.000000 23.000000 7.000000 20.000000
## [1687] 17.000000 7.000000 14.000000 31.000000 19.000000 11.000000
## [1693] 18.000000 12.000000 10.000000 18.000000 32.000000 13.000000
## [1699] 11.000000 20.000000 9.000000 5.000000 11.000000 25.000000
## [1705] 25.000000 11.000000 18.000000 20.000000 16.000000 10.000000
## [1711] 18.000000 11.000000 12.000000 16.000000 12.202093 12.672475
## [1717] 13.999122 16.407220 21.009409 13.831997 10.388916 12.338808
## [1723] 14.211520 14.265210 16.729410 21.381455 14.247531 10.841668
## [1729] 12.822814 13.386349 14.795993 17.277184 21.943124 14.820959
## [1735] 11.425693 13.417212 13.991760 15.413795 17.909327 22.591936
## [1741] 15.488913 12.138931 14.177735 14.800938 16.272105 18.816114
## [1747] 23.545196 16.485116 13.149117 15.194978 15.816551 17.275680
## [1753] 19.795631 24.487189 17.374913 13.971125 15.933036 17.789284
## [1759] 17.797064 20.185235 24.730777 17.459866 13.886888 15.671528
## [1765] 16.010306 17.168472 20.708359 23.740588 17.634850 12.569310
## [1771] 14.212082 14.428046 15.485481 17.614392 21.932457 14.471120
## [1777] 10.749413 12.430906 12.715108 13.869261 16.121639 20.587518
## [1783] 13.295308 9.760440 11.642385 12.136151 13.504183 19.308714
## [1789] 25.651555 17.721396 11.028872 12.237393 14.203414 15.681211
## [1795] 18.226178 21.611444 15.863984 12.489033 13.141934 13.684801
## [1801] 16.378640 18.776818 23.326004 23.326004 14.717583 11.136441
## [1807] 12.908224 13.230696 14.370613 16.557054 20.907386 13.453357
## [1813] 9.714917 11.357140 11.581599 12.658110 16.152939 19.179884
## [1819] 11.775981 9.460620 9.892483 10.275634 11.541674 13.917887
## [1825] 18.518282 11.369767 9.321052 11.359861 10.679323 13.544891
## [1831] 14.838164 19.672559 12.735003 9.534822 11.725969 12.498668
## [1837] 14.111445 16.784648 21.626628 14.660044 11.395900 13.490684
## [1843] 15.472902 16.935156 16.935156 19.437335 24.092812 13.464095
## [1849] 14.023392 14.476272 15.755347 18.090159 23.933108 16.645648
## [1855] 13.081878 13.570635 13.981692 15.247743 17.597530 22.146835
## [1861] 14.924594 11.446771 13.373370 13.899926 15.289403 17.765605
## [1867] 22.439272 15.334350 11.962007 13.977785 14.573231 16.007905
## [1873] 18.502899 23.167016 16.023100 12.582091 14.500193 14.970488
## [1879] 16.254916 18.577731 23.051599 15.703825 12.050295 13.752507
## [1885] 14.009049 15.087424 17.217359 21.516757 14.017777 10.240644
## [1891] 11.850564 12.049095 13.105892 15.251953 19.605532 12.198209
## [1897] 8.548702 10.319830 10.709928 11.984683 14.370473 18.980397
## [1903] 11.840476 8.463606 10.506658 11.162052 12.689732 15.310649
## [1909] 20.132921 13.178152 11.290454 13.453773 12.858350 15.767705
## [1915] 17.062487 21.856146 14.836604 11.515095 13.548316 14.129975
## [1921] 15.522714 17.951489 22.529614 15.284917 11.733626 13.537352
## [1927] 13.894517 15.072161 17.299216 21.692463 14.282612 10.588133
## [1933] 12.272204 12.534123 13.641117 15.821640 20.191375 12.779366
## [1939] 9.101929 10.819682 11.129056 12.294198 14.540388 20.315097
## [1945] 11.639408 8.031668 9.814958 10.183504 11.399685 13.687487
## [1951] 18.158636 10.838859 7.241877 9.026512 10.723197 11.926038
## [1957] 14.196000 14.196000 17.312131 9.972569 6.358654 8.131801
## [1963] 8.489989 9.699588 13.323638 16.472994 9.182094 5.633154
## [1969] 7.487966 7.944363 9.268040 11.685870 16.311907 9.173533
## [1975] 7.120329 7.806288 8.430973 9.921771 12.502132 18.117428
## [1981] 10.287527 7.028148 9.160623 9.877174 11.438145 15.400444
## [1987] 20.203817 13.206920 8.586502 10.669359 11.314113 13.617748
## [1993] 15.300393 20.813495 12.844975 9.414213 11.346974 15.679747
## [1999] 13.160980 16.871119 20.082021 12.828597 9.293840 11.140616
## [2005] 11.568118 12.843673 15.196042 19.741386 12.509394 9.017149
## [2011] 10.926123 11.433648 12.804772 15.265628 19.929477 12.822912
## [2017] 9.459807 11.498406 12.132865 13.625199 16.198720 20.964146
## [2023] 13.945868 10.655941 12.751208 13.424870 14.938435 18.850121
## [2029] 22.266131 16.551496 16.551496 16.551496 11.879561 13.913539
## [2035] 14.513406 15.942622 18.426534 23.078417 15.925633 13.818500
## [2041] 13.818500 14.412480 14.909294 16.239117 18.628738 23.192603
## [2047] 15.958964 12.443766 14.307667 14.747512 16.028201 18.376051
## [2053] 22.904837 15.641981 12.102489 13.946009 14.368359 15.633432
## [2059] 17.966610 22.480840 16.537819 12.982101 14.807965 13.876127
## [2065] 15.120479 17.431702 21.923194 14.622305 11.044279 12.849298
## [2071] 13.233985 14.463281 16.763804 22.584849 13.949716 10.379271
## [2077] 12.199311 12.606861 15.202032 17.541512 20.739410 13.493427
## [2083] 9.983935 13.205846 13.644157 16.812144 18.131508 20.591770
## [2089] 25.161081 17.965154 14.507385 16.395215 23.586571 18.279736
## [2095] 20.794749 25.413842 18.262623 14.844588 16.767481 17.304108
## [2101] 13.892474 14.889775 16.937126 9.807446 6.410786 8.355726
## [2107] 8.915812 10.347374 13.858835 18.531057 11.434510 8.094749
## [2113] 10.098304 10.717875 13.943085 16.568195 21.294939 14.248055
## [2119] 10.929790 12.946259 12.518597 13.999573 16.600909 21.288477
## [2125] 14.185836 8.531381 8.813957 11.449093 10.681653 13.140141
## [2131] 19.274353 12.000718 8.427573 10.163412 10.482244 11.641453
## [2137] 15.085920 18.250515 11.979810 6.195996 7.788429 7.985432
## [2143] 9.047516 11.243443 15.546553 8.087791 4.375684 6.022569
## [2149] 6.305283 7.482919 9.822079 14.293224 7.023757 3.518074
## [2155] 5.383875 5.892965 7.299122 13.524078 17.838215 9.511191
## [2161] 18.940428 3.599380 5.418052 6.923290 9.552201 13.088996
## [2167] 7.190967 3.820899 4.582571 5.102905 7.634785 10.082493
## [2173] 14.592110 14.592110 6.110796 2.500816 4.193174 4.463520
## [2179] 5.571172 7.784381 12.076661 6.402663 2.626770 4.729680
## [2185] 4.904316 5.956425 9.335169 13.127823 5.704605 3.223574
## [2191] 3.760844 4.129331 5.395677 7.851479 12.452845 5.325558
## [2197] 3.927560 5.953776 4.673919 8.205809 8.986966 13.846566
## [2203] 6.949476 3.793429 9.154661 9.950762 15.300792 18.072917
## [2209] 22.925133 15.978796 12.733755 14.794793 40.768600 42.237262
## [2215] 42.237262 25.995112 30.621284 15.044465 14.910535 15.316488
## [2221] 14.503020 16.866612 23.272171 15.940244 12.335341 12.891792
## [2227] 13.244922 14.476080 16.852985 24.569091 17.309352 13.801190
## [2233] 15.653500 16.139251 17.513356 20.038042 24.679334 17.560279
## [2239] 14.181129 16.145800 16.722795 18.163199 20.726164 25.375483
## [2245] 18.232878 14.798228 16.676036 17.136348 18.432698 20.827535
## [2251] 25.288707 17.942635 14.294421 15.954163 94.143429 15.174781
## [2257] 21.203031 19.801412 12.300913 9.760590 11.326187 11.510795
## [2263] 11.762241 13.977713 21.522516 11.548692 7.905668 9.636899
## [2269] 10.017933 11.306222 13.766432 18.366852 11.232550 8.505192
## [2275] 10.510643 11.158034 12.698913 18.136067 21.589342 14.635153
## [2281] 12.265397 14.383031 11.064256 13.820070 15.332144 20.100777
## [2287] 13.069412 9.738721 10.228735 10.781639 12.166723 14.647672
## [2293] 19.193487 11.931590 9.352327 11.097356 13.165496 14.339317
## [2299] 16.624786 20.994287 13.577983 6.865969 8.505799 8.756254
## [2305] 9.876402 12.133042 16.497075 9.096605 5.437007 7.127250
## [2311] 7.440718 8.633080 10.967682 17.114372 8.614530 5.030468
## [2317] 6.789756 10.544085 11.786429 14.159479 18.630142 10.307171
## [2323] 6.725712 8.477104 10.012897 11.231507 12.573785 12.573785
## [2329] 15.835438 8.499203 4.892112 6.624443 6.971672 8.192259
## [2335] 10.579401 13.872630 8.014704 6.479019 8.295628 7.378931
## [2341] 7.631225 10.125623 14.744530 7.614379 7.076088 5.976014
## [2347] 6.595775 8.102855 10.758754 22.862370 14.653108 11.398658
## [2353] 8.379323 9.078450 10.640913 15.468408 20.242766 13.228390
## [2359] 7.784764 9.800534 10.405179 13.434265 14.407213 25.530122
## [2365] 13.781342 10.317161 10.690464 31.743996 12.424520 19.419813
## [2371] 19.328460 12.042427 7.712627 9.490016 9.881989 11.397222
## [2377] 15.464620 19.985611 12.748945 6.438462 8.309289 8.813301
## [2383] 65.924107 6.974798 21.832181 14.749929 11.417909 13.442410
## [2389] 11.439045 12.971439 15.645490 22.248777 15.260049 12.002218
## [2395] 12.707067 13.390897 14.933510 20.253309 30.247981 16.174796
## [2401] 16.174796 16.174796 14.060235 16.052613 16.637130 18.069244
## [2407] 20.612935 25.237107 18.068698 13.399805 13.399805 16.478617
## [2413] 16.940877 18.256968 22.560677 27.086027 19.052430 15.518813
## [2419] 17.318206 14.204141 15.475497 17.876335 23.960363 16.685586
## [2425] 13.140765 14.934193 15.338906 16.610566 19.012832 23.513819
## [2431] 17.880481 14.333955 16.123106 14.879393 16.140724 18.529665
## [2437] 23.014665 15.722027 12.156004 14.177511 12.714762 13.958109
## [2443] 16.331698 25.290896 16.350129 12.782792 14.557011 14.947031
## [2449] 16.800462 17.176359 20.907544 13.649934 11.650025 14.256111
## [2455] 14.353161 14.364292 14.375422 14.386552 14.397682 14.408812
## [2461] 14.419943 14.431073 16.583802 14.453333 14.464464 14.475594
## [2467] 14.486724 14.497854 14.508984 14.520115 17.577698 17.588828
## [2473] 17.599959 17.611089 17.622219 17.633349 17.644479 17.655610
## [2479] 17.666740 17.677870 17.689000 17.700131 17.711261 17.722391
## [2485] 17.733521 17.744651 17.755782 17.766912 17.778042 17.789172
## [2491] 17.800302 17.811433 17.822563 17.833693 17.844823 17.855954
## [2497] 17.867084 20.019813 17.889344 17.900474 17.565455 17.576585
## [2503] 17.587715 17.598846 17.609976 17.621106 19.773835 17.643367
## [2509] 19.796096 17.665627 17.676757 17.687887 17.699018 17.710148
## [2515] 17.721278 17.732408 17.743538 17.754669 17.765799 17.776929
## [2521] 17.788059 17.799190 17.810320 17.821450 17.832580 17.843710
## [2527] 17.854841 20.790841 22.264407 21.544320 18.630579 16.157691
## [2533] 18.310420 18.321550 18.332680 16.202211 18.354941 18.366071
## [2539] 16.235602 16.246732 18.399462 18.410592 18.421722 18.421722
## [2545] 16.291253 16.302383 16.313514 16.324644 16.335774 16.346904
## [2551] 16.358034 16.369165 16.380295 16.391425 16.402555 16.413686
## [2557] 18.566415 16.435946 16.447076 18.599805 16.469337 16.480467
## [2563] 16.590927 16.602057 16.613187 16.624317 18.777047 18.788177
## [2569] 16.657708 18.810437 16.679969 16.691099 16.702229 16.713359
## [2575] 16.724489 16.735620 16.746750 16.757880 16.769010 16.780140
## [2581] 16.791271 16.802401 18.955130 18.966260 18.966260 18.977390
## [2587] 18.988521 19.010781 16.880312 16.891443 16.902573 16.913703
## [2593] 19.066432 19.757535 19.768665 17.638196 17.649327 17.660457
## [2599] 17.671587 17.682717 17.693847 17.704978 17.716108 17.727238
## [2605] 17.738368 17.749499 17.760629 17.771759 17.782889 17.794019
## [2611] 17.805150 17.816280 17.827410 17.838540 17.849670 17.860801
## [2617] 17.871931 17.883061 17.894191 17.905322 17.916452 17.927582
## [2623] 17.938712 17.949842 16.037926 16.049056 16.060186 16.071316
## [2629] 16.082447 16.093577 16.104707 16.115837 16.126967 16.138098
## [2635] 16.149228 16.160358 16.171488 16.182619 16.193749 16.204879
## [2641] 16.216009 16.227139 16.238270 16.249400 16.260530 16.271660
## [2647] 16.282790 16.293921 16.305051 16.316181 18.468910 18.480041
## [2653] 17.004368 19.157097 17.026628 17.037759 17.048889 17.060019
## [2659] 17.071149 17.082279 17.093410 17.104540 17.115670 17.126800
## [2665] 17.137930 17.149061 17.160191 17.171321 17.182451 17.193582
## [2671] 17.204712 17.215842 17.226972 17.238102 17.249233 17.260363
## [2677] 17.271493 17.282623 17.293753 17.304884 17.316014 17.327144
## [2683] 17.338274 15.290172 13.159704 13.170834 13.181964 13.193094
## [2689] 13.204225 13.215355 13.226485 13.237615 13.248745 13.259876
## [2695] 15.412605 15.423735 15.434865 15.434865 13.304396 13.315527
## [2701] 13.326657 13.337787 13.348917 13.360048 15.512777 13.382308
## [2707] 13.393438 13.404568 13.415699 13.426829 13.437959 13.449089
## [2713] 13.460219 13.471350 16.874016 14.743547 14.754678 14.765808
## [2719] 14.776938 15.519286 14.799199 14.810329 14.821459 14.832589
## [2725] 14.843719 16.996449 17.007579 17.018709 14.888240 14.899370
## [2731] 14.910501 15.652849 14.932761 15.675109 14.955022 14.966152
## [2737] 14.977282 19.323664 14.999542 17.152272 15.021803 15.032933
## [2743] 15.044063 15.055193 15.066324 17.424263 17.435394 17.446524
## [2749] 17.457654 17.468784 17.479914 17.491045 17.502175 17.513305
## [2755] 17.524435 17.535565 17.546696 17.557826 17.568956 17.580086
## [2761] 17.591217 17.602347 17.613477 17.624607 17.635737 17.646868
## [2767] 17.657998 19.810727 17.680258 19.832987 19.832987 19.832987
## [2773] 17.702519 17.713649 17.724779 17.735909 17.747040 19.728088
## [2779] 19.739218 21.891947 21.891947 19.761479 19.772609 19.783739
## [2785] 19.794869 19.805999 19.817130 19.828260 19.839390 19.850520
## [2791] 19.861651 19.872781 19.883911 19.895041 19.906171 19.917302
## [2797] 19.928432 19.939562 19.950692 19.961822 22.114552 22.125682
## [2803] 22.136812 20.006343 20.017474 20.028604 20.039734 20.050864
## [2809] 20.061994 18.248736 18.259866 18.270997 18.282127 20.434856
## [2815] 18.304387 18.315517 18.326648 18.337778 20.490507 20.501637
## [2821] 18.371169 18.382299 18.393429 20.546158